Scalable Inference of Overlapping Communities

  title={Scalable Inference of Overlapping Communities},
  author={Prem Gopalan and David M. Mimno and Sean Gerrish and Michael J. Freedman and David M. Blei},
We develop a scalable algorithm for posterior inference of overlapping communities in large networks. Our algorithm is based on stochastic variational inference in the mixed-membership stochastic blockmodel (MMSB). It naturally interleaves subsampling the network with estimating its community structure. We apply our algorithm on ten large, real-world networks with up to 60,000 nodes. It converges several orders of magnitude faster than the state-of-the-art algorithm for MMSB, finds hundreds of… CONTINUE READING
Highly Influential
This paper has highly influenced 12 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 89 citations. REVIEW CITATIONS

From This Paper

Figures, tables, and topics from this paper.
54 Citations
30 References
Similar Papers


Publications citing this paper.
Showing 1-10 of 54 extracted citations

89 Citations

Citations per Year
Semantic Scholar estimates that this publication has 89 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 30 references

A multiscale community blockmodel for network exploration

  • Qirong Ho, Ankur P. Parikh, Eric P. Xing
  • Journal of the American Statistical Association,
  • 2012
1 Excerpt

A survey of statistical network models

  • A. Goldenberg, A. Zheng, S. Fienberg, E. Airoldi
  • Foundations and Trends in Machine Learning, 2:129…
  • 2010
1 Excerpt

Bagrow , and Sune Lehmann . Link communities reveal multiscale complexity in networks

  • E. A. Leicht
  • Nature
  • 2010

Similar Papers

Loading similar papers…